Revista de investigación clínica

Landscape of clinical research is undergoing a profound transformation, driven by the power of big data. The traditional approach to clinical research, which relied on small, controlled studies, is giving way to a new era characterized by the collection, analysis, and interpretation of massive volumes of healthcare data. Big data has the potential to revolutionize clinical research in ways that were once unimaginable, offering unprecedented insights, accelerating drug discovery and ultimately improving patient care. In this article, we explore the pivotal role of big data in reshaping clinical research.

The landscape of clinical research is rapidly evolving, driven by the transformative power of digital technology. In the digital age, researchers have unprecedented access to innovative tools and data sources that are revolutionizing the way clinical studies are designed, conducted, and analyzed. This abstract explores the pivotal role of technology in advancing clinical research, highlighting its potential to accelerate progress in healthcare.

Health disparities, characterized by differences in health outcomes among various populations, continue to pose significant challenges to healthcare systems worldwide. These disparities are often rooted in complex socioeconomic, cultural and structural factors, making their resolution a multifaceted endeavor. In recent years, there has been a growing recognition of the pivotal role that clinical research plays in identifying and addressing health disparities. This abstract highlights the critical importance of inclusive clinical research practices as a promising approach to mitigate health disparities.

Clinical research, the dynamic bridge between scientific innovation and medical practice, plays a pivotal role in advancing the field of medicine. This comprehensive overview delves into the significance of clinical research, its fundamental processes, and its profound impact on patient care. Clinical research is the bedrock of evidence-based medicine, furnishing healthcare professionals with essential data to make informed treatment decisions. It acts as a catalyst for innovation and discovery, birthing new therapies and treatments that hold the potential to revolutionize healthcare. Moreover, it fosters a culture of quality improvement, steering healthcare systems toward evidence-based practices.

Background: Preterm births significantly contribute to mortality and the effect of preterm birth amongst some survivors may continue throughout life. These effects exert a heavy burden on families, society and the health system. According to the World Health Organization, the number of preterm born babies (<37 weeks) in Zambia for the year 2015 was 79,800 and the number of deaths in children below the age of 5 (0-59 months) due to direct preterm birth complications was 4,220. Aim: The development of innovative solutions for prevention of preterm births rely on a better understanding of the factors leading to preterm birth and it’s adverse outcomes. This research therefore aimed at determining the prevalence, risk factors and outcomes of preterm births at Mumbwa district hospital. The research findings will help assess and improve infrastructure for adequate care required for preterm babies thereby reducing neonatal mortality. This will in turn lead to improved medical care for pregnant women with risk factors that could cause premature births, thereby reducing neonatal mortality, chronic diseases as well as long term health expenditures. Methodology: A hospital based retrospective study was conducted at the maternity ward Mumbwa district hospital a level III hospital, from June to August 2020, this included all births at the hospital from 1st January 2018 to 31st December 2019. Data was collected using a data extraction sheet formulated to encompass maternal factors, obstetrics related factors, relevant medical history, mode of delivery and short term birth outcomes including measurements of birth weight, sex and condition at time of discharge, from labor ward registers. Data were entered into excel and exported to SPSS version 20 for analysis. Descriptive statistics like frequencies, means and cross tabulations were performed. Association between risk factors and preterm birth was evaluated using bivariate analysis. Results: In 2018 a total number of 2460 deliveries were conducted at Mumbwa district hospital. 126 (5.1%) of these deliveries were premature, 606 (25%) babies had low birth weight (<2500 g) and 28 (1.1%) neonatal deaths. In 2019 a total number of 1960 deliveries were conducted at Mumbwa district hospital. 135 (7%) of these deliveries were premature, 311 (15.9%) babies had low birth weight (<2500 g) and 12 (0.6%) neonatal deaths. The statistics show a reduction in total deliveries from 2460 to 1960 in 2018 and 2019 respectively, with a 1.9% increase in preterm births. There was a reduction in low birth weight (<2500) and a reduction in fetal deaths. According to the statistics 8 (6.3%) had history of previous cesarean section, 3 (2.4%) had bad obstetric history, 38 women had preterm births due to maternal, fatal and placental causes in 2018. According to the statistics 3 (2.2%) had history of previous cesarean section, 37 (4.57%) had bad obstetric history, 42 women had preterm births due to maternal, fatal and placental causes in 2019. The risk factors of preterm delivery at Mumbwa district hospital include maternal age (p=0.028), parity (p=0.039), previous obstetric history of cesarean section (0.012), bad obstetric history (0.027) and antepartum hemorrhage (0.001, 0.031). Furthermore, 87 (69%) and 107 (79.7%) preterm births were idiopathic in 2018 and 2019 respectively (p=0.001). In addition, preterm births are significantly associated with low birth weight (p<0.000). Conclusion and recommendations: Research results show an increase in preterm births indicating that it is among the major health concerns in Mumbwa district. Enhanced sensitization of women on early signs of preterm labour and encouraging them to seek medical attention promptly is cardinal. To efficiently implement programs aimed at reducing preterm births it is essential to incorporate data to action